An Adaptive Clustering Algorithm of Gaussian Curve Fitting the Histogram Based on K-Means

2019 ◽  
Author(s):  
Guangkun Ma ◽  
Jinzhu Yang ◽  
Xiaoxu Song ◽  
Hong Zhao
Keyword(s):  
Curve Fitting ◽  
Clustering Algorithm ◽  
Adaptive Clustering ◽  
Gaussian Curve

Using A Segmented Voltage Sweep Mode and A Gaussian Curve Fitting Method to Improve Heavy Metal Measurement System Performance

2012 ◽  
Vol 19 (2) ◽  
pp. 381-394
Author(s):  
José Pereira ◽  
Octavian Postolache ◽  
Pedro Girão
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Measurement System ◽  
Curve Fitting ◽  
System Performance ◽  
Redox Potentials ◽  
Voltage Sweep ◽  
Gaussian Curve ◽  
Fitting Method ◽  
Curve Fitting Method

Using A Segmented Voltage Sweep Mode and A Gaussian Curve Fitting Method to Improve Heavy Metal Measurement System PerformanceThis paper presents a voltammetric segmented voltage sweep mode that can be used to identify and measure heavy metals' concentrations. The proposed sweep mode covers a set of voltage ranges that are centered around the redox potentials of the metals that are under analysis. The heavy metal measurement system can take advantage of the historical database of measurements to identify the metals with higher concentrations in a given geographical area, and perform a segmented sweep around predefined voltage ranges or, alternatively, the system can perform a fast linear voltage sweep to identify the voltammetric current peaks and then perform a segmented voltage sweep around the set of voltages that are associated with the voltammetric current peaks. The paper also includes the presentation of two auto-calibration modes that can be used to improve system's reliability and proposes the usage of a Gaussian curve fitting of voltammetric data to identify heavy metals and to evaluate their concentrations. Several simulation and experimental results, that validate the theoretical expectations, are also presented in the paper.

ANALYSIS OF MEASURED THALASSINOIDES ISP. DIAMETERS ACROSS THE CRETACEOUS-PALEOGENE BOUNDARY AT SEWELL, NJ (USA) USING GAUSSIAN CURVE-FITTING

2018 ◽  
Author(s):  
David E. Grandstaff ◽  
◽  
Logan A. Wiest ◽  
Ilya V. Buynevich ◽  
Dennis O. Terry
Keyword(s):  
Curve Fitting ◽  
Gaussian Curve

An Improved Ad Hoc Network Communication Based on Cluster

2013 ◽  
Vol 660 ◽  
pp. 184-189 ◽  
Author(s):  
Yan Zhai ◽  
Xing Wei ◽  
Lei Liu ◽  
Liao Yuan Wu
Keyword(s):  
Ad Hoc Network ◽  
Clustering Algorithm ◽  
Ad Hoc ◽  
Cluster Head ◽  
Directional Antennas ◽  
Directional Antenna ◽  
Gateway Node ◽  
Adaptive Clustering ◽  
Communication Method ◽  
Hoc Networks

In order to tackle the data transmission bottlenecks of the gateway node in clustering Ad hoc Networks, the paper proposes a communication method. Firstly, DMAC (Distributed and Mobility-Adaptive Clustering) algorithm and Omni-directional antenna is well introduced and discussed. Then the ICMMDA (The Inter-cluster Communication Method based on Directional Antennas) policy building virtual channels between two hops away cluster-head and using directional antenna is brought about. Lastly, the simulation shows that the method can reduce the end-to-end delay between two clusters and improve the network throughput.

scholarly journals Research on the Method of Coke Optical Tissue Segmentation Based on Adaptive Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Huaiguang Liu ◽  
Liheng Zhang ◽  
Shiyang Zhou ◽  
Li Fang
Keyword(s):  
Mahalanobis Distance ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Adaptive Threshold ◽  
Segmentation Method ◽  
Tissue Segmentation ◽  
Adaptive Clustering ◽  
Morphological Filtering ◽  
Key Factor ◽  
The Relationship

The microstructure is the key factor for quality discriminate of coke. In view of the characteristics of coke optical tissue (COT), a segmentation method of coke microstructures based on adaptive clustering was proposed. According to the strategy of multiresolution, adaptive threshold binarization and morphological filtering were carried out on COT images with lower resolution. The contour of the COT body was detected through the relationship checking between contours in the binary image, and hence, COT pixels were picked out to cluster for tissue segmentation. In order to get the optimum segmentation for each tissue, an advanced K -means method with adaptive clustering centers was provided according to the Calinski-Harabasz score. Meanwhile, Euclidean distance was substituted with Mahalanobis distance between each pixel in HSV space to improve the accuracy. The experimental results show that compared with the traditional K -means algorithm, FCM algorithm, and Meanshift algorithm, the adaptive clustering algorithm proposed in this paper is more accurate in the segmentation of various tissue components in COT images, and the accuracy of tissue segmentation reaches 94.3500%.

Characterizing Diesel Fuel Spray Cone Angle From Back-Scattered Imaging by Fitting Gaussian Profiles to Radial Spray Intensity Distributions

2012 ◽  
Vol 134 (6) ◽  
Author(s):  
Jaclyn E. Johnson ◽  
Jeffrey D. Naber ◽  
Seong-Young Lee
Keyword(s):  
Charge Density ◽  
Diesel Fuel ◽  
Curve Fitting ◽  
Cone Angle ◽  
Injection Pressure ◽  
Fuel Spray ◽  
Spray Cone Angle ◽  
Spray Cone ◽  
Gaussian Curve

Quantifying fuel spray properties including penetration, cone angle, and vaporization processes sheds light on fuel-air mixing phenomenon, which governs subsequent combustion and emissions formation in diesel engines. Accurate experimental determination of these spray properties is a challenge but imperative to validate computational fluid dynamic (CFD) models for combustion prediction. This study proposes a new threshold independent method for determination of spray cone angle when using Mie back-scattering optical diagnostics to visualize diesel sprays in an optically accessible constant volume vessel. Test conditions include the influence of charge density (17.6 and 34.9 kg/m3) at 1990 bar injection pressure, and the influence of injection pressure (990, 1370, and 1980 bar) at a charge density of 34.8 kg/m3 on diesel fuel spray formation from a multi-hole injector into nitrogen at a temperature of 100 °C. Conventional thresholding to convert an image to black and white for processing and determination of cone angle is threshold subjective. As an alternative, an image processing method was developed, which fits a Gaussian curve to the intensity distribution of the spray at radial spray cross-sections and uses the resulting parameters to define the spray edge and hence cone angle. This Gaussian curve fitting methodology is shown to provide a robust method for cone angle determination, accounting for reductions in intensity at the radial spray edge. Results are presented for non-vaporizing sprays using this Gaussian curve fitting method and compared to the conventional thresholding based method.

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